Name
Title | ||
|---|---|---|
Learning the human longitudinal control behavior with a modular hierarchical Bayesian Mixture-of-Behaviors model |
Abstract | ||
|---|---|---|
Modeling drivers' behavior is believed to be essential for the rapid prototyping of error-compensating assistance systems. Various authors proposed control-theoretic and production-system models. These models are handcrafted in a top-down software engineering process. Here we propose a machine-learning alternative by estimating stochastic driver models from behavior traces. They are more robust than their non-stochastic predecessors. In this paper we present a Bayesian Autonomous Driver Mixture-of-Behaviors (BAD MoB) model for the longitudinal control of human drivers in an inner-city traffic scenario. It is learnt on the basis of multivariate time-series obtained in simulator studies. Percepts relevant for longitudinal control were included in the model by a structure-learning method using Bayesian information criteria. Besides mimicking human driver behavior we suggest using the model for prototyping intelligent assistance systems with human-like behavior. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IVS.2011.5940530 | Intelligent Vehicles Symposium |
Keywords | Field | DocType |
behavioural sciences,belief networks,driver information systems,error compensation,learning (artificial intelligence),road traffic,software prototyping,stochastic processes,time series,BAD MoB model,Bayesian autonomous driver mixture-of-behaviors model,Bayesian information criteria,error compensating assistance system,human longitudinal control behavior learning,inner-city traffic,intelligent assistance system,machine learning,modular hierarchical bayesian mixture-of-behaviors model,multivariate time series,rapid prototyping,stochastic driver model estimation,structure learning method,top-down software engineering process | Rapid prototyping,Bayesian information criterion,Multivariate statistics,Software prototyping,Stochastic process,Software development process,Artificial intelligence,Modular design,Engineering,Machine learning,Bayesian probability | Conference |
ISSN | ISBN | Citations |
1931-0587 | 978-1-4577-0890-9 | 4 |
PageRank | References | Authors |
0.54 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark Eilers | 1 | 20 | 3.70 |
| Claus Möbus | 2 | 58 | 15.18 |